scikit-learn
diff --git a/‎sklearn/linear_model/stochastic_gradient.py
Lines changed: 9 additions & 1 deletion b/‎sklearn/linear_model/stochastic_gradient.py
Lines changed: 9 additions & 1 deletion
diff --git a/‎sklearn/linear_model/tests/test_sgd.py
Lines changed: 32 additions & 0 deletions b/‎sklearn/linear_model/tests/test_sgd.py
Lines changed: 32 additions & 0 deletions
@@ -6,6 +6,7 @@
 
 import numpy as np
 import scipy.sparse as sp
+import warnings
 
 from abc import ABCMeta, abstractmethod
 
@@ -543,12 +544,19 @@ def fit(self, X, y, coef_init=None, intercept_init=None,
 
         sample_weight : array-like, shape (n_samples,), optional
             Weights applied to individual samples.
-            If not provided, uniform weights are assumed.
+            If not provided, uniform weights are assumed. These weights will
+            be multiplied with class_weight (passed through the
+            contructor) if class_weight is specified
 
         Returns
         -------
         self : returns an instance of self.
         """
+        if class_weight is not None:
+            warnings.warn("You are trying to set class_weight through the fit "
+                          "method, which will be deprecated in version "
+                          "v0.17 of scikit-learn. Pass the class_weight into "
+                          "the constructor instead.", DeprecationWarning)
         return self._fit(X, y, alpha=self.alpha, C=1.0,
                          loss=self.loss, learning_rate=self.learning_rate,
                          coef_init=coef_init, intercept_init=intercept_init,
 
@@ -14,6 +14,7 @@
 from sklearn.utils.testing import assert_false, assert_true
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_raises_regexp
+from sklearn.utils.testing import assert_warns_message
 
 from sklearn import linear_model, datasets, metrics
 from sklearn.base import clone
@@ -597,6 +598,37 @@ def test_wrong_class_weight_format(self):
         clf = self.factory(alpha=0.1, n_iter=1000, class_weight=[0.5])
         clf.fit(X, Y)
 
+    def test_class_weight_warning(self):
+        """Tests that class_weight passed through fit raises warning.
+           This test should be removed after deprecating support for this"""
+
+        clf = self.factory()
+        warning_message = ("You are trying to set class_weight through the "
+                           "fit "
+                           "method, which will be deprecated in version "
+                           "v0.17 of scikit-learn. Pass the class_weight into "
+                           "the constructor instead.")
+        assert_warns_message(DeprecationWarning,
+                             warning_message,
+                             clf.fit, X4, Y4,
+                             class_weight=1)
+
+    def test_weights_multiplied(self):
+        """Tests that class_weight and sample_weight are multiplicative"""
+        class_weights = {1: .6, 2: .3}
+        sample_weights = np.random.random(Y4.shape[0])
+        multiplied_together = np.copy(sample_weights)
+        multiplied_together[Y4 == 1] *= class_weights[1]
+        multiplied_together[Y4 == 2] *= class_weights[2]
+
+        clf1 = self.factory(alpha=0.1, n_iter=20, class_weight=class_weights)
+        clf2 = self.factory(alpha=0.1, n_iter=20)
+
+        clf1.fit(X4, Y4, sample_weight=sample_weights)
+        clf2.fit(X4, Y4, sample_weight=multiplied_together)
+
+        assert_array_equal(clf1.coef_, clf2.coef_)
+
     def test_auto_weight(self):
         """Test class weights for imbalanced data"""
         # compute reference metrics on iris dataset that is quite balanced by